• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.415-425
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• 2000

We proposed a new classification algorithm based on bootstrap sampling and pairwise coupling method. Also, for comparing the accuracy of a proposed algorithm with those of old methods, we conducted classification with waveform data and others.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.877-882
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• 1993

An adaptive EEG waveform detection is presented. The method is based on a layered process model. The model allows the bilateral information exchange across the layers. The criteria for the waveform detection and epoch-wise classification can be adapted according to the higher layer context information embedded in a wider range of adjacent signals. The designed system is experimentally tested to show the adaptive operation of the waveform detection.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.181.4-181
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• 2001

In this study, we have developed the digital hardware system which performs signal processing necessary for the filtering to eliminate noises by inputting pulse wave signals from the sensor group. With a view to obtain clinically effective information, we analyzed structural elements of pulse waveform and, thus, conducted a systematic classification. What is more, this study has conducted researches in the web-based diagnosis data management system of pulse waveform as well as the method of transmitting the data of pulse waveform. In order to set the standard for the documents of the pulse ...

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.2
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• pp.133-140
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• 2018

In electronic warfare(EW), low probability of intercept(LPI) radar signal is a survival technique. Accordingly, identification techniques of the LPI radar waveform have became significant recently. In this paper, classification and extracting parameters techniques for 7 intrapulse modulated radar signals are introduced. We propose a technique of classifying intrapulse modulated radar signals using Convolutional Neural Network(CNN). The time-frequency image(TFI) obtained from Choi-William Distribution(CWD) is used as the input of CNN without extracting the extra feature of each intrapulse modulated radar signals. In addition a method to extract the intrapulse radar modulation parameters using binary image processing is introduced. We demonstrate the performance of the proposed intrapulse radar waveform identification system. Simulation results show that the classification system achieves a overall correct classification success rate of 90 % or better at SNR = -6 dB and the parameter extraction system has an overall error of less than 10 % at SNR of less than -4 dB.

• Journal of the Korea Society of Computer and Information
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• v.24 no.1
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• pp.33-39
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• 2019

In this paper, we propose deep convolutional neural network(CNN) with bottleneck structure which improves the performance of earthquake classification. In order to address all possible forms of earthquakes including micro-earthquakes and artificial-earthquakes as well as large earthquakes, we need a representation and classifier that can effectively discriminate seismic waveforms in adverse conditions. In particular, to robustly classify seismic waveforms even in low snr, a deep CNN with 1x1 convolution bottleneck structure is proposed in raw seismic waveforms. The representative experimental results show that the proposed method is effective for noisy seismic waveforms and outperforms the previous state-of-the art methods on domestic earthquake database.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.4
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• pp.167-172
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• 2014

In this paper we propose a waveform design algorithm that localizes the maximum output power in the target direction. We extend existing monostatic radar optimal waveform design schemes to bistatic multiple-input multiple-output (MIMO) radar systems. The algorithm simultaneously calculates the direction of departure (DoD) and the direction of arrival (DoA) using a two-dimensional multiple signal classification (MUSIC) method, and successfully localizes the maximum transmitted power to the target locations by exploiting the calculated DoD. The simulation results confirm the performance of the proposed algorithm.

• Journal of the Korean Society for Nondestructive Testing
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• v.31 no.2
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• pp.165-173
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• 2011

The acoustic emission(AE) technique is a well established method to carry out structural health monitoring(SHM) of large structures. However, the real-time monitoring of the crack growth in the roller coaster support structures is not easy since the vehicle operation produces very large noise as well as crack growth. In this investigation, we present the waveform parameter filtering method to classify acoustic sources in real-time. This method filtrates only the AE hits by the target acoustic source as passing hits in a specific parameter band. According to various acoustic sources, the waveform parameters were measured and analyzed to verify the present filtering method. Also, the AE system employing the waveform parameter filter was manufactured and applied to the roller coaster support structure in an actual amusement park.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.7
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• pp.962-968
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• 2018

In this study, we developed novel indicators to assess postoperative pain based on PPG derivative waveform. As the candidate indicator of postoperative pain assessment, the time from the start of beating to the n-th peak($T_n$) and the n-th peak amplitude($A_n$) of the PPG derivative were selected. In order to verify derived indicators, each candidate indicator was derived from the PPG of 78 subjects before and after surgery, and it was confirmed whether significant changes were observed after surgery. Logistic classification was performed with each proposed indicator to calculate the pain classification accuracy, then the classification performance was compared with SPI(Surgical Pleth Index, GE Healthcare, Chicago, US). The results showed that there were significant differences(p < 0.01) in all indicators except for $T_3$ and $A_3$. The coefficient of variation(CV) of every time-related indicators were lower than the CV of SPI(30.43%), however, the CV in amplitude-related parameters were higher than that of SPI. Among the candidate indicators, amplitude of the first peak, $A_1$, showed that highest accuracy in post-operative pain classification, 68.72%, and it is 15.53% higher than SPI.

• Journal of Power System Engineering
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• v.7 no.2
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• pp.29-35
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• 2003

The monitoring and diagnostics of the rotating machinery have been received considerable attention for many years. The objectives are to classify the machinery condition and to find out the cause of abnormal condition. This paper describes a classification method of diagnosing the small reciprocating compressor for refrigerators using the artificial neural network and the wavelet transform. In order to extract salient features, the wavelet transform are used from primary noise signals. Since the wavelet transform decomposes raw time-waveform signals into two respective parts in the time space and frequency domain, more and better features can be obtained easier than time-waveform analysis. In the training phase for classification, self-organizing feature map(SOFM) and learning vector quantization(LVQ) are applied, and the accuracies of them ate compared with each other. This paper is focused on the development of an advanced signal classifier to automatize the vibration signal pattern recognition. This method is verified by small reciprocating compressors, for refrigerator and normal and abnormal conditions are classified with high flexibility and reliability.

• Speech Sciences
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• v.4 no.2
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• pp.41-54
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• 1998

Speech signals are, depending on the characteristics of waveform, classified as voiced sound, unvoiced sound, and silence. Voiced sound, produced by an air flow generated by the vibration of the vocal cords, is quasi-periodic, while unvoiced sound, produced by a turbulent air flow passed through some constriction in the vocal tract, is noise-like. Silence represents the ambient noise signal during the absence of speech. The need for deciding whether a given segment of a speech waveform should be classified as voiced, unvoiced, or silence has arisen in many speech analysis systems. In this paper, a voiced/unvoiced/silence classification algorithm using spectral change in the wavelet transformed signal is proposed and then, experimental results are demonstrated with our discussions.